Path and speed optimization fallback mechanism for autonomous vehicles

ABSTRACT

According to some embodiments, a system calculates a first trajectory based on a map and a route information. The system performs a path optimization based on the first trajectory, traffic rules, and an obstacle information describing obstacles perceived by the ADV. The path optimization is performed by performing a spline curve based path optimization on the first trajectory, determining whether a result of the spline curve based path optimization satisfies a first predetermined condition, performing a finite element based path optimization on the first trajectory in response to determining that the result of the spline curve based path optimization does not satisfy the first predetermined condition, performing a speed optimization based on a result of the path optimization, and generating a second trajectory based on the path optimization and the speed optimization to control the ADV.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to operatingautonomous vehicles. More particularly, embodiments of the disclosurerelate to path and speed optimization fallback mechanisms for autonomousdriving vehicles (ADVs).

BACKGROUND

Vehicles operating in an autonomous mode (e.g., driverless) can relieveoccupants, especially the driver, from some driving-relatedresponsibilities. When operating in an autonomous mode, the vehicle cannavigate to various locations using onboard sensors, allowing thevehicle to travel with minimal human interaction or in some caseswithout any passengers.

Vehicles can operate in an autonomous mode by a planned drivingtrajectory. The driving trajectory can be generated at path and speedplanning stages. Both path and speed planning can rely heavily onoptimization solvers to solve optimization problems. However,optimization problems may fail due to numerical issues, e.g., thequadratic programming (QP) problem may not converge within a finitenumber of iterations or elapsed time. Therefore, there is a need to havea fallback mechanism to improve the robustness of the optimizationsolver to tolerate some failures due to numerical issues.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure are illustrated by way of example and notlimitation in the figures of the accompanying drawings in which likereferences indicate similar elements.

FIG. 1 is a block diagram illustrating a networked system according toone embodiment.

FIG. 2 is a block diagram illustrating an example of a sensor andcontrol system using by an autonomous vehicle according to oneembodiment.

FIGS. 3A-3B are block diagrams illustrating examples of a perception andplanning system used by an autonomous vehicle according to someembodiments.

FIG. 4 is a block diagram illustrating an example of a decision andplanning process according to one embodiment.

FIG. 5 is a block diagram illustrating an example of a planning moduleaccording to one embodiment.

FIG. 6 is a block diagram illustrating an example of a planning cycleaccording to one embodiment.

FIG. 7 is an example of a SL (station-lateral) map for spline curvebased QP optimization according to one embodiment.

FIG. 8 is an example of a SL map for finite element based QPoptimization according to one embodiment.

FIG. 9 is a flow diagram illustrating a method according to oneembodiment.

FIG. 10 is a block diagram illustrating a data processing systemaccording to one embodiment.

DETAILED DESCRIPTION

Various embodiments and aspects of the disclosures will be describedwith reference to details discussed below, and the accompanying drawingswill illustrate the various embodiments. The following description anddrawings are illustrative of the disclosure and are not to be construedas limiting the disclosure. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentdisclosure. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present disclosures.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin conjunction with the embodiment can be included in at least oneembodiment of the disclosure. The appearances of the phrase “in oneembodiment” in various places in the specification do not necessarilyall refer to the same embodiment.

According to some embodiments, a system performs a spline curve basedpath and speed optimization to generate a path curve and speed curve.The system includes a fallback mechanism, e.g., a finite element basedoptimization in each of the path and speed optimization. In oneembodiment, the system calculates a rough path profile and a rough speedprofile (representing a first trajectory) based on a map and a routeinformation. The system performs a path optimization based on the firsttrajectory, traffic rules, and an obstacle information describingobstacles perceived by the ADV. The path optimization is performed byperforming a spline curve based path optimization on the firsttrajectory, determining whether a result of the spline curve based pathoptimization satisfy a first predetermined condition, performing afinite element based path optimization on the first trajectory inresponse to determining that the result of the spline curve based pathoptimization does not satisfy the first predetermined condition,performing a speed optimization based on a result of the pathoptimization, and generating a second trajectory based on the pathoptimization and the speed optimization, where the second trajectory isused to control the ADV. In one embodiment, a path optimization and/or aspeed optimization may include a quadratic programming (QP)optimization.

FIG. 1 is a block diagram illustrating an autonomous vehicle networkconfiguration according to one embodiment of the disclosure. Referringto FIG. 1, network configuration 100 includes autonomous vehicle 101that may be communicatively coupled to one or more servers 103-104 overa network 102. Although there is one autonomous vehicle shown, multipleautonomous vehicles can be coupled to each other and/or coupled toservers 103-104 over network 102. Network 102 may be any type ofnetworks such as a local area network (LAN), a wide area network (WAN)such as the Internet, a cellular network, a satellite network, or acombination thereof, wired or wireless. Server(s) 103-104 may be anykind of servers or a cluster of servers, such as Web or cloud servers,application servers, backend servers, or a combination thereof. Servers103-104 may be data analytics servers, content servers, trafficinformation servers, map and point of interest (MPOI) severs, orlocation servers, etc.

An autonomous vehicle refers to a vehicle that can be configured to inan autonomous mode in which the vehicle navigates through an environmentwith little or no input from a driver. Such an autonomous vehicle caninclude a sensor system having one or more sensors that are configuredto detect information about the environment in which the vehicleoperates. The vehicle and its associated controller(s) use the detectedinformation to navigate through the environment. Autonomous vehicle 101can operate in a manual mode, a full autonomous mode, or a partialautonomous mode.

In one embodiment, autonomous vehicle 101 includes, but is not limitedto, perception and planning system 110, vehicle control system 111,wireless communication system 112, user interface system 113, and sensorsystem 115. Autonomous vehicle 101 may further include certain commoncomponents included in ordinary vehicles, such as, an engine, wheels,steering wheel, transmission, etc., which may be controlled by vehiclecontrol system 111 and/or perception and planning system 110 using avariety of communication signals and/or commands, such as, for example,acceleration signals or commands, deceleration signals or commands,steering signals or commands, braking signals or commands, etc.

Components 110-115 may be communicatively coupled to each other via aninterconnect, a bus, a network, or a combination thereof. For example,components 110-115 may be communicatively coupled to each other via acontroller area network (CAN) bus. A CAN bus is a vehicle bus standarddesigned to allow microcontrollers and devices to communicate with eachother in applications without a host computer. It is a message-basedprotocol, designed originally for multiplex electrical wiring withinautomobiles, but is also used in many other contexts.

Referring now to FIG. 2, in one embodiment, sensor system 115 includes,but it is not limited to, one or more cameras 211, global positioningsystem (GPS) unit 212, inertial measurement unit (IMU) 213, radar unit214, and a light detection and range (LIDAR) unit 215. GPS system 212may include a transceiver operable to provide information regarding theposition of the autonomous vehicle. IMU unit 213 may sense position andorientation changes of the autonomous vehicle based on inertialacceleration. Radar unit 214 may represent a system that utilizes radiosignals to sense objects within the local environment of the autonomousvehicle. In some embodiments, in addition to sensing objects, radar unit214 may additionally sense the speed and/or heading of the objects.LIDAR unit 215 may sense objects in the environment in which theautonomous vehicle is located using lasers. LIDAR unit 215 could includeone or more laser sources, a laser scanner, and one or more detectors,among other system components. Cameras 211 may include one or moredevices to capture images of the environment surrounding the autonomousvehicle. Cameras 211 may be still cameras and/or video cameras. A cameramay be mechanically movable, for example, by mounting the camera on arotating and/or tilting a platform.

Sensor system 115 may further include other sensors, such as, a sonarsensor, an infrared sensor, a steering sensor, a throttle sensor, abraking sensor, and an audio sensor (e.g., microphone). An audio sensormay be configured to capture sound from the environment surrounding theautonomous vehicle. A steering sensor may be configured to sense thesteering angle of a steering wheel, wheels of the vehicle, or acombination thereof. A throttle sensor and a braking sensor sense thethrottle position and braking position of the vehicle, respectively. Insome situations, a throttle sensor and a braking sensor may beintegrated as an integrated throttle/braking sensor.

In one embodiment, vehicle control system 111 includes, but is notlimited to, steering unit 201, throttle unit 202 (also referred to as anacceleration unit), and braking unit 203. Steering unit 201 is to adjustthe direction or heading of the vehicle. Throttle unit 202 is to controlthe speed of the motor or engine that in turn controls the speed andacceleration of the vehicle. Braking unit 203 is to decelerate thevehicle by providing friction to slow the wheels or tires of thevehicle. Note that the components as shown in FIG. 2 may be implementedin hardware, software, or a combination thereof.

Referring back to FIG. 1, wireless communication system 112 is to allowcommunication between autonomous vehicle 101 and external systems, suchas devices, sensors, other vehicles, etc. For example, wirelesscommunication system 112 can wirelessly communicate with one or moredevices directly or via a communication network, such as servers 103-104over network 102. Wireless communication system 112 can use any cellularcommunication network or a wireless local area network (WLAN), e.g.,using WiFi to communicate with another component or system. Wirelesscommunication system 112 could communicate directly with a device (e.g.,a mobile device of a passenger, a display device, a speaker withinvehicle 101), for example, using an infrared link, Bluetooth, etc. Userinterface system 113 may be part of peripheral devices implementedwithin vehicle 101 including, for example, a keyword, a touch screendisplay device, a microphone, and a speaker, etc.

Some or all of the functions of autonomous vehicle 101 may be controlledor managed by perception and planning system 110, especially whenoperating in an autonomous driving mode. Perception and planning system110 includes the necessary hardware (e.g., processor(s), memory,storage) and software (e.g., operating system, planning and routingprograms) to receive information from sensor system 115, control system111, wireless communication system 112, and/or user interface system113, process the received information, plan a route or path from astarting point to a destination point, and then drive vehicle 101 basedon the planning and control information. Alternatively, perception andplanning system 110 may be integrated with vehicle control system 111.

For example, a user as a passenger may specify a starting location and adestination of a trip, for example, via a user interface. Perception andplanning system 110 obtains the trip related data. For example,perception and planning system 110 may obtain location and routeinformation from an MPOI server, which may be a part of servers 103-104.The location server provides location services and the MPOI serverprovides map services and the POIs of certain locations. Alternatively,such location and MPOI information may be cached locally in a persistentstorage device of perception and planning system 110.

While autonomous vehicle 101 is moving along the route, perception andplanning system 110 may also obtain real-time traffic information from atraffic information system or server (TIS). Note that servers 103-104may be operated by a third party entity. Alternatively, thefunctionalities of servers 103-104 may be integrated with perception andplanning system 110. Based on the real-time traffic information, MPOIinformation, and location information, as well as real-time localenvironment data detected or sensed by sensor system 115 (e.g.,obstacles, objects, nearby vehicles), perception and planning system 110can plan an optimal route and drive vehicle 101, for example, viacontrol system 111, according to the planned route to reach thespecified destination safely and efficiently.

Server 103 may be a data analytics system to perform data analyticsservices for a variety of clients. In one embodiment, data analyticssystem 103 includes data collector 121 and machine learning engine 122.Data collector 121 collects driving statistics 123 from a variety ofvehicles, either autonomous vehicles or regular vehicles driven by humandrivers. Driving statistics 123 include information indicating thedriving commands (e.g., throttle, brake, steering commands) issued andresponses of the vehicles (e.g., speeds, accelerations, decelerations,directions) captured by sensors of the vehicles at different points intime. Driving statistics 123 may further include information describingthe driving environments at different points in time, such as, forexample, routes (including starting and destination locations), MPOIs,road conditions, weather conditions, etc.

Based on driving statistics 123, machine learning engine 122 generatesor trains a set of rules, algorithms, and/or predictive models 124 for avariety of purposes. In one embodiment, for example, algorithms 124 mayinclude an optimization method to optimize path planning and speedplanning. The optimization method may include a set of cost functionsand polynomial functions to represent path segments or time segments.These functions can be uploaded onto the autonomous driving vehicle tobe used to generate a smooth path at real time.

FIGS. 3A and 3B are block diagrams illustrating an example of aperception and planning system used with an autonomous vehicle accordingto one embodiment. System 300 may be implemented as a part of autonomousvehicle 101 of FIG. 1 including, but is not limited to, perception andplanning system 110, control system 111, and sensor system 115.Referring to FIGS. 3A-3B, perception and planning system 110 includes,but is not limited to, localization module 301, perception module 302,prediction module 303, decision module 304, planning module 305, controlmodule 306, and routing module 307.

Some or all of modules 301-307 may be implemented in software, hardware,or a combination thereof. For example, these modules may be installed inpersistent storage device 352, loaded into memory 351, and executed byone or more processors (not shown). Note that some or all of thesemodules may be communicatively coupled to or integrated with some or allmodules of vehicle control system 111 of FIG. 2. Some of modules 301-307may be integrated together as an integrated module. For example,decision module 304 and planning module 305 may be integrated as asingle module.

Localization module 301 determines a current location of autonomousvehicle 300 (e.g., leveraging GPS unit 212) and manages any data relatedto a trip or route of a user. Localization module 301 (also referred toas a map and route module) manages any data related to a trip or routeof a user. A user may log in and specify a starting location and adestination of a trip, for example, via a user interface. Localizationmodule 301 communicates with other components of autonomous vehicle 300,such as map and route information 311, to obtain the trip related data.For example, localization module 301 may obtain location and routeinformation from a location server and a map and POI (MPOI) server. Alocation server provides location services and an MPOI server providesmap services and the POIs of certain locations, which may be cached aspart of map and route information 311. While autonomous vehicle 300 ismoving along the route, localization module 301 may also obtainreal-time traffic information from a traffic information system orserver.

Based on the sensor data provided by sensor system 115 and localizationinformation obtained by localization module 301, a perception of thesurrounding environment is determined by perception module 302. Theperception information may represent what an ordinary driver wouldperceive surrounding a vehicle in which the driver is driving. Theperception can include the lane configuration (e.g., straight or curvelanes), traffic light signals, a relative position of another vehicle, apedestrian, a building, crosswalk, or other traffic related signs (e.g.,stop signs, yield signs), etc., for example, in a form of an object.

Perception module 302 may include a computer vision system orfunctionalities of a computer vision system to process and analyzeimages captured by one or more cameras in order to identify objectsand/or features in the environment of autonomous vehicle. The objectscan include traffic signals, road way boundaries, other vehicles,pedestrians, and/or obstacles, etc. The computer vision system may usean object recognition algorithm, video tracking, and other computervision techniques. In some embodiments, the computer vision system canmap an environment, track objects, and estimate the speed of objects,etc. Perception module 302 can also detect objects based on othersensors data provided by other sensors such as a radar and/or LIDAR.

For each of the objects, prediction module 303 predicts how the objectwill behave under the circumstances. The prediction is performed basedon the perception data perceiving the driving environment at the pointin time in view of a set of map/route information 311 and traffic rules312. For example, if the object is a vehicle at an opposing directionand the current driving environment includes an intersection, predictionmodule 303 will predict whether the vehicle will likely move straightforward or make a turn. If the perception data indicates that theintersection has no traffic light, prediction module 303 may predictthat the vehicle may have to fully stop prior to enter the intersection.If the perception data indicates that the vehicle is currently at aleft-turn only lane or a right-turn only lane, prediction module 303 maypredict that the vehicle will more likely make a left turn or right turnrespectively.

For each of the objects, decision module 304 makes a decision regardinghow to handle the object. For example, for a particular object (e.g.,another vehicle in a crossing route) as well as its metadata describingthe object (e.g., a speed, direction, turning angle), decision module304 decides how to encounter the object (e.g., overtake, yield, stop,pass). Decision module 304 may make such decisions according to a set ofrules such as traffic rules or driving rules 312, which may be stored inpersistent storage device 352.

Based on a decision for each of the objects perceived, planning module305 plans a path or route for the autonomous vehicle, as well as drivingparameters (e.g., distance, speed, and/or turning angle). That is, for agiven object, decision module 304 decides what to do with the object,while planning module 305 determines how to do it. For example, for agiven object, decision module 304 may decide to pass the object, whileplanning module 305 may determine whether to pass on the left side orright side of the object. Planning and control data is generated byplanning module 305 including information describing how vehicle 300would move in a next moving cycle (e.g., next route/path segment). Forexample, the planning and control data may instruct vehicle 300 to move10 meters at a speed of 30 mile per hour (mph), then change to a rightlane at the speed of 25 mph.

Based on the planning and control data, control module 306 controls anddrives the autonomous vehicle, by sending proper commands or signals tovehicle control system 111, according to a route or path defined by theplanning and control data. The planning and control data includesufficient information to drive the vehicle from a first point to asecond point of a route or path using appropriate vehicle settings ordriving parameters (e.g., throttle, braking, and turning commands) atdifferent points in time along the path or route.

In one embodiment, the planning phase is performed in a number ofplanning cycles, also referred to as command cycles, such as, forexample, in every time interval of 100 milliseconds (ms). For each ofthe planning cycles or command cycles, one or more control commands willbe issued based on the planning and control data. That is, for every 100ms, planning module 305 plans a next route segment or path segment, forexample, including a target position and the time required for the ADVto reach the target position. Alternatively, planning module 305 mayfurther specify the specific speed, direction, and/or steering angle,etc. In one embodiment, planning module 305 plans a route segment orpath segment for the next predetermined period of time such as 5seconds. For each planning cycle, planning module 305 plans a targetposition for the current cycle (e.g., next 5 seconds) based on a targetposition planned in a previous cycle. Control module 306 then generatesone or more control commands (e.g., throttle, brake, steering controlcommands) based on the planning and control data of the current cycle.

Note that decision module 304 and planning module 305 may be integratedas one module. Decision module 304/planning module 305 may include anavigation system or functionalities of a navigation system to determinea driving path for the autonomous vehicle. For example, the navigationsystem may determine a series of speeds and directional headings toeffect movement of the autonomous vehicle along a path thatsubstantially avoids perceived obstacles while generally advancing theautonomous vehicle along a roadway-based path leading to an ultimatedestination. The destination may be set according to user inputs viauser interface system 113. The navigation system may update the drivingpath dynamically while the autonomous vehicle is in operation. Thenavigation system can incorporate data from a GPS system and one or moremaps so as to determine the driving path for the autonomous vehicle.Decision module 304/planning module 305 may further include a collisionavoidance system or functionalities of a collision avoidance system toidentify, evaluate, and avoid or otherwise negotiate potential obstaclesin the environment of the autonomous vehicle. For example, the collisionavoidance system may effect changes in the navigation of the autonomousvehicle by operating one or more subsystems in control system 111 toundertake swerving maneuvers, turning maneuvers, braking maneuvers, etc.The collision avoidance system may automatically determine feasibleobstacle avoidance maneuvers on the basis of surrounding trafficpatterns, road conditions, etc. The collision avoidance system may beconfigured such that a swerving maneuver is not undertaken when othersensor systems detect vehicles, construction barriers, etc. in theregion adjacent the autonomous vehicle that would be swerved into. Thecollision avoidance system may automatically select the maneuver that isboth available and maximizes safety of occupants of the autonomousvehicle. The collision avoidance system may select an avoidance maneuverpredicted to cause the least amount of acceleration in a passenger cabinof the autonomous vehicle.

Routing module 307 can generate reference routes, for example, from mapinformation such as information of road segments, vehicular lanes ofroad segments, and distances from lanes to curb. For example, a road canbe divided into sections or segments {A, B, and C} to denote three roadsegments. Three lanes of road segment A can be enumerated {A1, A2, andA3}. A reference route is generated by generating reference points alongthe reference route. For example, for a vehicular lane, routing module307 can connect midpoints of two opposing curbs or extremities of thevehicular lane provided by a map data. Based on the midpoints andmachine learning data representing collected data points of vehiclespreviously driven on the vehicular lane at different points in time,routing module 307 can calculate the reference points by selecting asubset of the collected data points within a predetermined proximity ofthe vehicular lane and applying a smoothing function to the midpoints inview of the subset of collected data points.

Based on reference points or lane reference points, routing module 307may generate a reference line by interpolating the reference points suchthat the generated reference line is used as a reference line forcontrolling ADVs on the vehicular lane. In some embodiments, a referencepoints table and a road segments table representing the reference linesare downloaded in real-time to ADVs such that the ADVs can generatereference lines based on the ADVs' geographical location and drivingdirection. For example, in one embodiment, an ADV can generate areference line by requesting routing service for a path segment by apath segment identifier representing an upcoming road section aheadand/or based on the ADV's GPS location. Based on a path segmentidentifier, a routing service can return to the ADV reference pointstable containing reference points for all lanes of road segments ofinterest. ADV can look up reference points for a lane for a path segmentto generate a reference line for controlling the ADV on the vehicularlane.

As described above, route or routing module 307 manages any data relatedto a trip or route of a user. The user of the ADV specifies a startingand a destination location to obtain trip related data. Trip relateddata includes route segments and a reference line or reference points ofthe route segment. For example, based on route map info 311, routemodule 307 generates a route or road segments table and a referencepoints table. The reference points are in relations to road segmentsand/or lanes in the road segments table. The reference points can beinterpolated to form one or more reference lines to control the ADV. Thereference points can be specific to road segments and/or specific lanesof road segments.

For example, a road segments table can be a name-value pair to includeprevious and next road lanes for road segments A-D. E.g., a roadsegments table may be: {(A1, B1), (B1, C1), (C1, D1)} for road segmentsA-D having lane 1. A reference points table may include reference pointsin x-y coordinates for road segments lanes, e.g., {(A1, (x1, y1)), (B1,(x2, y2)), (C1, (x3, y3)), (D1, (x4, y4))}, where A1 . . . D1 refers tolane 1 of road segments A-D, and (x1, y1) (x4, y4) are correspondingreal world coordinates. In one embodiment, road segments and/or lanesare divided into a predetermined length such as approximately 200 meterssegments/lanes. In another embodiment, road segments and/or lanes aredivided into variable length segments/lanes depending on road conditionssuch as road curvatures. In some embodiments, each road segment and/orlane can include several reference points. In some embodiments,reference points can be converted to other coordinate systems, e.g.,latitude-longitude.

In some embodiments, reference points can be converted into a relativecoordinates system, such as station-lateral (SL) coordinates of a SL(station-lateral) map. A station-lateral coordinate system is acoordinate system that references a fixed reference point to follow areference line. For example, a (S, L)=(1, 0) coordinate can denote onemeter ahead of a stationary point (i.e., the reference point) on thereference line with zero meter lateral offset. A (S, L)=(2, 1) referencepoint can denote two meters ahead of the stationary reference pointalong the reference line and an one meter lateral offset from thereference line, e.g., offset to the left by one meter. SL map refers toa station-lateral map based on a reference line or driving trajectory inSL coordinates. The SL map can map the reference line or drivingtrajectory to a two dimensional real word longitudinal-latitudinalcoordinate map.

In one embodiment, decision module 304 generates a rough path profilebased on a reference line provided by routing module 307 and based onobstacles and/or traffic information perceived by the ADV, surroundingthe ADV. The rough path profile can be a part of path/speed profiles 313which may be stored in persistent storage device 352. The rough pathprofile is generated by selecting points along the reference line. Foreach of the points, decision module 304 moves the point to the left orright (e.g., candidate movements) of the reference line based on one ormore obstacle decisions on how to encounter the object, while the restof points remain steady. The candidate movements can be performediteratively using dynamic programming to path candidates in search of apath candidate with a lowest path cost using cost functions, as part ofcosts functions 315 of FIG. 3A, thereby generating a rough path profile.Examples of cost functions include costs based on: a curvature of aroute path, a distance from the ADV to perceived obstacles, and adistance of the ADV to the reference line. In one embodiment, thegenerated rough path profile includes a station-lateral map, as part ofSL maps/ST (station-time) graphs 314 which may be stored in persistentstorage devices 352. ST graph or station-time graph describes areference line or driving trajectory in station-time coordinates. The STgraph plots a distance along a reference line versus an elapsed time.

In one embodiment, decision module 304 generates a rough speed profile(as part of path/speed profiles 313) based on the generated rough pathprofile. The rough speed profile indicates the best speed at aparticular point in time controlling the ADV. Similar to the rough pathprofile, candidate speeds at different points in time are iterated usingdynamic programming to find speed candidates (e.g., speed up or slowdown) with a lowest speed cost based on cost functions, as part of costsfunctions 315 of FIG. 3A, in view of obstacles perceived by the ADV. Therough speed profile decides whether the ADV should overtake or avoid anobstacle, and to the left or right of the obstacle. In one embodiment,the rough speed profile includes a station-time (ST) graph (as part ofSL maps/ST graphs 314). Station-time graph indicates a distancetravelled with respect to time.

In one embodiment, the rough path profile is recalculated by optimizinga path cost function (as part of cost functions 315) using quadraticprogramming (QP). In one embodiment, the recalculated rough path profileincludes a station-lateral map (as part of SL maps/ST graphs 314). Inone embodiment, planning module 305 recalculates the rough speed profileusing quadratic programming (QP) to optimize a speed cost function (aspart of cost functions 315). In one embodiment, the recalculated roughspeed profile includes a station-time graph (as part of SL maps/STgraphs 314).

Note that some or all of the components as shown and described above maybe implemented in software, hardware, or a combination thereof. Forexample, such components can be implemented as software installed andstored in a persistent storage device, which can be loaded and executedin a memory by a processor (not shown) to carry out the processes oroperations described throughout this application. Alternatively, suchcomponents can be implemented as executable code programmed or embeddedinto dedicated hardware such as an integrated circuit (e.g., anapplication specific IC or ASIC), a digital signal processor (DSP), or afield programmable gate array (FPGA), which can be accessed via acorresponding driver and/or operating system from an application.Furthermore, such components can be implemented as specific hardwarelogic in a processor or processor core as part of an instruction setaccessible by a software component via one or more specificinstructions.

FIG. 4 is a block diagram illustrating an example of a decision andplanning process according to one embodiment. FIG. 5 is a block diagramillustrating an example of a planning module according to oneembodiment. Referring to FIG. 4, decision and planning process 400includes localization/perception data 401, path decision process 403,speed decision process 405, path planning process 407, speed planningprocess 409, aggregator 411, and trajectory calculator 413.

Path decision process 403 and speed decision process 405 may beperformed respectively by decision module 304 of FIG. 3A. Referring backto FIG. 4, path decision process 403 can generate a rough path profileas an initial constraint for the path/speed planning processes 407 and409 using dynamic programming. Note, dynamic programming (or dynamicoptimization) is a mathematical optimization method that breaks down aproblem to be solved into a sequence of value functions, solving each ofthese value functions just once and storing their solutions. The nexttime the same value function occurs, the previous computed solution issimply looked up saving computation time instead of recomputing itssolution. For example, from traffic rules, a reference line provided byrouting module 307, and obstacles perceived by the ADV, path decisionprocess 403 can decide how the perceived obstacles are handled (i.e.,ignore, overtake, yield, stop, pass), as part of a rough path profile.

In one embedment, the rough path profile is generated by a cost functionconsisting of costs based on: a curvature of path and a distance fromthe reference line and/or reference points to the perceived obstacles.Points on the reference line are selected and are moved to the left orright of the reference lines as candidate movements representing pathcandidates. Each of the candidate movements has an associated cost. Theassociated costs for candidate movements of one or more points on thereference line can be solved using dynamic programming for an optimalcost sequentially, one point at a time. In one embodiment, decisionmodule 304 generates a station-lateral (SL) map as part of the roughpath profile. Here, the perceived obstacles can be modeled as SLboundaries of the SL map. A SL map is a two-dimensional geometric map(similar to an x-y coordinate plane) that includes obstacles information(or SL boundaries) perceived by the ADV. The generated SL map lays outan ADV path for controlling the ADV.

Speed decision process 405 can generate a rough speed profile as aninitial constraint for the path/speed planning processes 407 and 409using dynamic programming. For example, speed decision process 405 cangenerate a rough speed profile to control when to speed up and/or slowdown the ADV based on a speed state (e.g., speed up or slow down) of theADV and any traffic information. Decision module 304 can generate astation-time (ST) graph as part of the rough speed profiles 313.

Referring to FIGS. 4-5, path planning process 407 may be performed bypath planning module 521. Path planning module 521 can include splinecurve based QP optimizer 525 and failsafe QP optimizer 527. Pathplanning process 407 or path planning module 521 can use a rough pathprofile (e.g., a station-lateral map) as the initial constraint torecalculate an optimal reference line using quadratic programming (QP)via spline curve based QP optimizer 525. Quadratic programming involvesminimizing or maximizing an objective function (e.g., a quadraticfunction with several variables) subject to bounds, linear equality, andinequality constraints. One difference between dynamic programming andquadratic programming is that quadratic programming optimizes allcandidate movements for all points on the reference line at once. Notethat throughout this application, QP optimization is utilized as anexample of path and/or speed optimization; however, other optimizers canalso be utilized.

Referring to FIG. 5, spline curve based optimizer 525 (as part of QPmodule 540) can initialize piecewise polynomials connecting adjacentcontrol points or some interpolated control points of a rough pathprofile provided by path decision process 403. Spline curve basedoptimizer 525 can add a set of joint smoothness constraints (e.g., firstorder, second order, third order, etc. constraints to join adjacentpiecewise polynomials) to improve a smoothness of the number ofpiecewise polynomials, ensuring polynomial segments between adjacentcontrol points are joint together smoothly. Spline curve based optimizer525 can select a target function with a set of kernels or weightingfunctions, which the optimizer will target on. Based on the target costfunction, optimizer 525 can recalculate an optimal path curve byminimizing a path cost using quadratic programming optimization.

Referring to FIG. 5, failsafe QP Optimizer 527 (as part of QP module540) can also perform a QP optimization for control points or someinterpolated control points of a rough path profile provided by pathdecision process 403. In one embodiment, failsafe QP optimizer 527includes a finite element optimizer to perform a finite elementoptimization. Finite element method or optimization is a numericalmethod for solving problems by subdividing a larger problem intosmaller, simpler equations. The simpler equations, modeling smallerparts or finite elements, are then assembled into a larger system ofequations to model the entire system. The larger systems of equationsare solved to approximate a solution. Here, optimizer 527 can optimizeor solve a QP problem of reducing a discontinuity between adjacentcontrol points. In one embodiment, optimizer 527 reduces a discontinuityof curvature between every adjacent control point to approximate asolution with an overall minimized discontinuity of curvature betweenevery adjacent control point. In another embodiment, optimizer 527reduces a change in angles between adjacent control points toapproximate a solution with an overall minimized discontinuity or changein angles between adjacent control points. The approximated solutionwould then be the optimal path curve (e.g., a SL map).

Referring to FIG. 4, speed planning process 409 can be performed byspeed planning module 523 of FIG. 5. Referring to FIG. 5, speed planningmodule 523 can include spline curve based QP optimizer 531 and failsafeQP optimizer 533. Speed planning process 409 or speed planning module523 can use a rough speed profile (e.g., a station-time graph as part ofstation-time graph 531) and results from path planning process 407(e.g., an optimal path curve) as initial conditions to calculate anoptimal station-time curve.

Referring to FIG. 5, spline curve based optimizer 531 (as part of QPmodule 540) can initialize piecewise polynomials connecting adjacentcontrol points or some interpolated control points of a rough speedprofile provided by speed decision process 405 in view of the optimalpath curve. Spline curve based optimizer 531 can add a set of jointsmoothness constraints (e.g., first order, second order, third order,etc. constraints to join adjacent piecewise polynomials) to guarantee asmoothness of the number of piecewise polynomials, ensuring polynomialsegments between adjacent control points are joint together smoothly.Spline curve based optimizer 531 can select a target cost function witha set of kernels or weighting functions, which the optimizer will targeton. Based on the target cost function, optimizer 531 can recalculate aspline curve, by minimizing a speed cost using spline curve based QPoptimization, for an optimal speed curve.

Referring to FIG. 5, failsafe QP optimizer 533 (as part of QP module540) can also optimize control points or some interpolated controlpoints of a rough speed profile provided by speed decision process 405in view of the optimal path curve. In one embodiment, failsafe QPoptimizer 533 includes a finite element optimizer to optimize thecontrol points by reducing a change between adjacent control points. Inon embodiment, optimizer 527 reduces changes in accelerations and speedsbetween adjacent control points to approximate a solution, e.g., anoptimal speed curve.

Referring to FIG. 4, aggregator 411 performs the function of aggregatingthe path and speed planning results. For example, in one embodiment,aggregator 411 can combine the ST graph (e.g., optimal speed curve) andthe SL map (e.g., optimal path curve) into a SLT graph. In anotherembodiment, aggregator 411 can interpolate (or fill in additionalpoints) based on 2 consecutive points on a SL map or ST graph. Inanother embodiment, aggregator 411 can translate control points from (S,L) coordinates to (x, y) coordinates. Trajectory generator 413 cancalculate the final trajectory (e.g., a driving trajectory) based on theSLT graph, or its translated equivalent, to control the ADV. Forexample, based on the SLT graph provided by aggregator 411, trajectorygenerator 413 calculates a list of (x, y, T) points indicating at whattime should the ADV pass a particular (x, y) coordinate.

Thus, path decision process 403 and speed decision process 405 are togenerate a rough path profile and a rough speed profile taking intoconsideration obstacles and/or traffic conditions. Given all the pathand speed decisions regarding the obstacles, path planning process 407and speed planning process 409 are to optimize the rough path profileand the speed profile using QP programming (spline curve based QPoptimizer or failsafe QP optimizer such as finite element based QPoptimizer) to generate an optimal trajectory with minimum path costand/or speed cost.

FIG. 6 illustrates an example of a planning cycle for optimizationalgorithms with a failsafe mechanism according to one embodiment.Referring to FIG. 6, planning cycle 600 includes processes 1-5.Processes 1-2 can be part of path planning process 407 of FIG. 4.Processes 1-2 can be performed by spline curve based QP optimizer 525and failsafe QP optimizer 527 of path planning module 521 of FIG. 5respectively. Processes 3-4 can be part of speed planning process 409 ofFIG. 4. Processes 3-4 can be performed by spline curve based QPoptimizer 531 and failsafe QP optimizer 533 of speed planning module 523of FIG. 5 respectively. Process 5 can be performed by aggregator 411 ofFIG. 4.

At process 1, processing logic performs a spline curve based path QPoptimization, if the optimization is a success (e.g., an outputsatisfies a predetermined condition), the processing logic continues toprocess 3. If process 1 optimization is not a success (e.g., theoptimization problem does not converge in a predetermined number offinite iterations, the optimization algorithm encounters an error, orcannot complete for any reasons), then processing logic continues atprocess 2. At process 2, processing logic performs a finite elementbased path QP optimization, i.e., as a failsafe optimization, andcontinues at process 3. At process 3, processing logic performs a splinecurve based speed QP optimization, if the optimization is a success, theprocessing logic continues at process 5. If process 3 optimization isnot a success (e.g., the optimization problem does not converge in apredetermined number of finite iterations, the optimization algorithmencounters an error or cannot complete for any reasons), then processinglogic continues at process 4. At process 4, processing logic performs afinite element based speed QP optimization, i.e., as a failsafeoptimization, and continues at process 5. At process 5, processing logicaggregates the planning results from the path and speed optimizations togenerate a driving trajectory.

FIG. 7 illustrates an example of a rough path profile (SL map) of a roadsegment according to one embodiment. Referring to FIG. 7, S path 700includes control points 701-704. Control points 701-704 provide forpiecewise polynomials 711-713 to generate a spline via spline curvebased QP optimizer 525 of path planning module 521 of FIG. 5. A splineis a curve represented by one or more (e.g., piecewise) polynomialsjoint together to form the curve. A path cost for the spline curve711-713 is optimized using QP optimization for a degree of smoothness ordiscontinuity. For example, in one embodiment, each polynomial functionwithin the spline can be:l(s)=p ₀ +p ₁ s+p ₂ s ² + . . . +p _(n) s ^(n),where s, l represents a station-lateral one dimensional (s, l) geometriccoordinate for a polynomial to the nth order, and p_(0 . . . n) arecoefficients of the polynomial to be solved.

The piecewise polynomials can be bounded by inequality constraints,which restrict a space which the polynomials must pass through, andequality constraints to joint adjacent piecewise polynomials whileensuring a degree of smoothness between the adjacent piecewisepolynomials. The system performs a quadratic programming (QP)optimization on a target function such that a total cost of the targetfunction reaches a minimum while the set of constraints are satisfied togenerate an optimal path curve.

Once a path is optimized, speed piecewise polynomials, representative ofspeed curves of the vehicle between each of the control points can bemodeled in a station-time (ST) graph by a general piecewise polynomial,via spline curve based QP optimizer 531 of speed planning module 523 ofFIG. 5. For example, in one embodiment, each piecewise polynomial withinthe speed spline can be:f(t)=a _(0i) +a _(1i) ×t+a _(2i) ×t ² +a _(3i) ×t ³ +a _(4i) ×t ⁴ +a_(5i) ×t ⁵where a_(0i), . . . , a_(5i) are coefficients of a fifth degreepolynomial, t is time in the segment ranging from 0 to total length ofthe segment, s is the accumulated distance over t. Note, the piecewisepolynomials can be of any degrees, or combination thereof, and is notlimited to a fifth degree polynomial. The piecewise polynomials ofadjacent control points can then be bounded by inequality constraints,which restrict a speed (e.g., maximum or minimum speed limits) which thepiecewise polynomials must satisfy, and equality constraints to jointadjacent piecewise polynomials while ensuring a degree of smoothnessbetween adjacent piecewise polynomials. The system performs a quadraticprogramming (QP) optimization on a speed target function such that atotal cost of the speed target function reaches a minimum while the setof constraints are satisfied to generate an optimal speed curve. Oncethe optimal speed curve is generated, the optimized ST graph and the SLmap can be aggregated to generate a trajectory to control an ADV.

The spline curve based QP optimization however may not converge in afinite number of iterations or fail due to optimization errors. FIG. 8illustrates an example of a SL map for finite element based QPoptimization as failsafe mechanisms according to one embodiment.Referring to FIG. 8, in one embodiment, an optimal path curve isgenerated, via failsafe QP optimizer 527 of path planning module 521 ofFIG. 5, by minimizing an overall change in curvature/direction/anglebetween adjacent control points of an SL map. In this case, a change inthe overall curvature can be subdivided into smaller problems ofindividual changes in curvatures or angles between control points701-704. These individual changes can be iteratively traversed to reacha global minimum for a global minimum in discontinuity between each pairof adjacent control points. For example, an angle cost function to beminimized can be:

${cost} = {\sum\limits_{i}\left\lbrack {\theta_{p_{i + 3} - p_{i + 2}} - \theta_{p_{i + 1} - p_{i}}} \right\rbrack^{2}}$where θ_(p) _(i+3) _(−p) _(i+2) represents a change in angle betweenadjacent control points p_(i+3) and p_(i+2). Similarly, θ_(p) _(i+1)_(−p) _(i) represents a change in angle between adjacent control pointsp_(i+1) and p_(i).

The path having a global minimum in discontinuity in curvatures orangles between adjacent points would then be the optimized path curve.Having the optimized path (either from the failsafe QP module 527 orspline curved based QP module 525), a finite element based speed QPoptimization problem can be set up, via failsafe QP module 533 of FIG.5, based on control points along the optimized path and a rough speedprofile. A cost function to be minimized for a finite element basedspeed QP optimization can be:speed cost=Σ_(points)α(acceleration)²+Σ_(points)β(speed−speed target)²,where the speeds cost are summed over all time progression points, α andβ are weight factors, acceleration denotes an acceleration value or acost to change speed between two adjacent points, speed denotes a speedvalue at a control point, and speed target denotes a desired targetspeed of the vehicle at the control point. A speed curve having a globalminimum in speed cost between adjacent points would then be theoptimized speed curve. The optimized speed curve and the optimized pathcan be aggregated to generate a driving trajectory to control an ADV.

FIG. 9 is a flow diagram illustrating a method to control an ADVaccording to one embodiment. Processing 900 may be performed byprocessing logic which may include software, hardware, or a combinationthereof. For example, process 900 may be performed by planning module305 of FIGS. 3A-3B. Referring to FIG. 9, at block 901, processing logiccalculates a first trajectory (e.g., a path profile and a speed profile)based on map and route information. At block 902, processing logicperforms a path optimization based on the first trajectory, trafficrules, and an obstacle information describing obstacles perceived by theADV. The path optimization including, at block 903, performing a splinecurve based path quadratic programming (QP) optimization on the firsttrajectory. At block 904, determining whether a result of the splinecurve based path QP optimization satisfy a first predeterminedcondition. At block 905, performing a finite element based path QPoptimization on the first trajectory in response to determining that theresult of the spline curve based path QP optimization does not satisfythe first predetermined condition. At block 906, performing a speed QPoptimization based on a result of the path QP optimization. At block907, generating a second trajectory based on the path QP optimizationand the speed QP optimization, wherein the second trajectory is used tocontrol the ADV.

In one embodiment, performing a speed QP optimization includesperforming a spline curve based speed QP optimization on the firsttrajectory in view of an optimized path, determining whether a result ofthe spline curve based speed QP optimization satisfy a secondpredetermined condition, and performing a finite element based speed QPoptimization on the first trajectory in view of the optimized path, inresponse to determining that the result of the spline curve based speedQP optimization does not satisfy the second predetermined condition. Inanother embodiment, the speed QP optimization is performed based on aninitial speed of the ADV and a speed limit. In another embodiment, theoptimized path includes a radial path and the speed QP optimization isperformed based on a radius of the radial path.

In one embodiment, determining the spline curve based path QPoptimization satisfy a first predetermined condition includesdetermining if a number of iterative calculations of the spline curvebased path QP optimization exceeds a predetermined number. In oneembodiment, determining the spline curve based path QP optimizationsatisfy a first predetermined condition includes determining if a timespent for the spline curve based path QP optimization exceeds apredetermined period of time. In one embodiment, performing a finiteelement based path QP optimization includes segmenting the firsttrajectory into a number of path points and minimizing discontinuitiesbetween adjacent path points in the plurality of path points.

FIG. 10 is a block diagram illustrating an example of a data processingsystem which may be used with one embodiment of the disclosure. Forexample, system 1500 may represent any of data processing systemsdescribed above performing any of the processes or methods describedabove, such as, for example, perception and planning system 110 orservers 103-104 of FIG. 1. System 1500 can include many differentcomponents. These components can be implemented as integrated circuits(ICs), portions thereof, discrete electronic devices, or other modulesadapted to a circuit board such as a motherboard or add-in card of thecomputer system, or as components otherwise incorporated within achassis of the computer system.

Note also that system 1500 is intended to show a high level view of manycomponents of the computer system. However, it is to be understood thatadditional components may be present in certain implementations andfurthermore, different arrangement of the components shown may occur inother implementations. System 1500 may represent a desktop, a laptop, atablet, a server, a mobile phone, a media player, a personal digitalassistant (PDA), a Smartwatch, a personal communicator, a gaming device,a network router or hub, a wireless access point (AP) or repeater, aset-top box, or a combination thereof. Further, while only a singlemachine or system is illustrated, the term “machine” or “system” shallalso be taken to include any collection of machines or systems thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein.

In one embodiment, system 1500 includes processor 1501, memory 1503, anddevices 1505-1508 connected via a bus or an interconnect 1510. Processor1501 may represent a single processor or multiple processors with asingle processor core or multiple processor cores included therein.Processor 1501 may represent one or more general-purpose processors suchas a microprocessor, a central processing unit (CPU), or the like. Moreparticularly, processor 1501 may be a complex instruction set computing(CISC) microprocessor, reduced instruction set computing (RISC)microprocessor, very long instruction word (VLIW) microprocessor, orprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processor 1501 may alsobe one or more special-purpose processors such as an applicationspecific integrated circuit (ASIC), a cellular or baseband processor, afield programmable gate array (FPGA), a digital signal processor (DSP),a network processor, a graphics processor, a communications processor, acryptographic processor, a co-processor, an embedded processor, or anyother type of logic capable of processing instructions.

Processor 1501, which may be a low power multi-core processor socketsuch as an ultra-low voltage processor, may act as a main processingunit and central hub for communication with the various components ofthe system. Such processor can be implemented as a system on chip (SoC).Processor 1501 is configured to execute instructions for performing theoperations and steps discussed herein. System 1500 may further include agraphics interface that communicates with optional graphics subsystem1504, which may include a display controller, a graphics processor,and/or a display device.

Processor 1501 may communicate with memory 1503, which in one embodimentcan be implemented via multiple memory devices to provide for a givenamount of system memory. Memory 1503 may include one or more volatilestorage (or memory) devices such as random access memory (RAM), dynamicRAM (DRAM), synchronous DRAM (SDRAM), static RAM (SRAM), or other typesof storage devices. Memory 1503 may store information includingsequences of instructions that are executed by processor 1501, or anyother device. For example, executable code and/or data of a variety ofoperating systems, device drivers, firmware (e.g., input output basicsystem or BIOS), and/or applications can be loaded in memory 1503 andexecuted by processor 1501. An operating system can be any kind ofoperating systems, such as, for example, Robot Operating System (ROS),Windows® operating system from Microsoft®, Mac OS®/iOS® from Apple,Android® from Google®, LINUX, UNIX, or other real-time or embeddedoperating systems.

System 1500 may further include 10 devices such as devices 1505-1508,including network interface device(s) 1505, optional input device(s)1506, and other optional 10 device(s) 1507. Network interface device1505 may include a wireless transceiver and/or a network interface card(NIC). The wireless transceiver may be a WiFi transceiver, an infraredtransceiver, a Bluetooth transceiver, a WiMax transceiver, a wirelesscellular telephony transceiver, a satellite transceiver (e.g., a globalpositioning system (GPS) transceiver), or other radio frequency (RF)transceivers, or a combination thereof. The NIC may be an Ethernet card.

Input device(s) 1506 may include a mouse, a touch pad, a touch sensitivescreen (which may be integrated with display device 1504), a pointerdevice such as a stylus, and/or a keyboard (e.g., physical keyboard or avirtual keyboard displayed as part of a touch sensitive screen). Forexample, input device 1506 may include a touch screen controller coupledto a touch screen. The touch screen and touch screen controller can, forexample, detect contact and movement or break thereof using any of aplurality of touch sensitivity technologies, including but not limitedto capacitive, resistive, infrared, and surface acoustic wavetechnologies, as well as other proximity sensor arrays or other elementsfor determining one or more points of contact with the touch screen.

IO devices 1507 may include an audio device. An audio device may includea speaker and/or a microphone to facilitate voice-enabled functions,such as voice recognition, voice replication, digital recording, and/ortelephony functions. Other IO devices 1507 may further include universalserial bus (USB) port(s), parallel port(s), serial port(s), a printer, anetwork interface, a bus bridge (e.g., a PCI-PCI bridge), sensor(s)(e.g., a motion sensor such as an accelerometer, gyroscope, amagnetometer, a light sensor, compass, a proximity sensor, etc.), or acombination thereof. Devices 1507 may further include an imagingprocessing subsystem (e.g., a camera), which may include an opticalsensor, such as a charged coupled device (CCD) or a complementarymetal-oxide semiconductor (CMOS) optical sensor, utilized to facilitatecamera functions, such as recording photographs and video clips. Certainsensors may be coupled to interconnect 1510 via a sensor hub (notshown), while other devices such as a keyboard or thermal sensor may becontrolled by an embedded controller (not shown), dependent upon thespecific configuration or design of system 1500.

To provide for persistent storage of information such as data,applications, one or more operating systems and so forth, a mass storage(not shown) may also couple to processor 1501. In various embodiments,to enable a thinner and lighter system design as well as to improvesystem responsiveness, this mass storage may be implemented via a solidstate device (SSD). However in other embodiments, the mass storage mayprimarily be implemented using a hard disk drive (HDD) with a smalleramount of SSD storage to act as a SSD cache to enable non-volatilestorage of context state and other such information during power downevents so that a fast power up can occur on re-initiation of systemactivities. Also a flash device may be coupled to processor 1501, e.g.,via a serial peripheral interface (SPI). This flash device may providefor non-volatile storage of system software, including BIOS as well asother firmware of the system.

Storage device 1508 may include computer-accessible storage medium 1509(also known as a machine-readable storage medium or a computer-readablemedium) on which is stored one or more sets of instructions or software(e.g., module, unit, and/or logic 1528) embodying any one or more of themethodologies or functions described herein. Processingmodule/unit/logic 1528 may represent any of the components describedabove, such as, for example, planning module 305 of FIG. 3, and/orspline curve based QP optimizers 525, 531, and failsafe QP optimizers527, 533 of FIG. 5. Processing module/unit/logic 1528 may also reside,completely or at least partially, within memory 1503 and/or withinprocessor 1501 during execution thereof by data processing system 1500,memory 1503 and processor 1501 also constituting machine-accessiblestorage media. Processing module/unit/logic 1528 may further betransmitted or received over a network via network interface device1505.

Computer-readable storage medium 1509 may also be used to store the somesoftware functionalities described above persistently. Whilecomputer-readable storage medium 1509 is shown in an exemplaryembodiment to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of instructions. The terms“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing or encoding a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present disclosure. The term“computer-readable storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media, or any other non-transitory machine-readable medium.

Processing module/unit/logic 1528, components and other featuresdescribed herein can be implemented as discrete hardware components orintegrated in the functionality of hardware components such as ASICS,FPGAs, DSPs or similar devices. In addition, processingmodule/unit/logic 1528 can be implemented as firmware or functionalcircuitry within hardware devices. Further, processing module/unit/logic1528 can be implemented in any combination hardware devices and softwarecomponents.

Note that while system 1500 is illustrated with various components of adata processing system, it is not intended to represent any particulararchitecture or manner of interconnecting the components; as suchdetails are not germane to embodiments of the present disclosure. Itwill also be appreciated that network computers, handheld computers,mobile phones, servers, and/or other data processing systems which havefewer components or perhaps more components may also be used withembodiments of the disclosure.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as those set forth in the claims below, refer to the actionand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

Embodiments of the disclosure also relate to an apparatus for performingthe operations herein. Such a computer program is stored in anon-transitory computer readable medium. A machine-readable mediumincludes any mechanism for storing information in a form readable by amachine (e.g., a computer). For example, a machine-readable (e.g.,computer-readable) medium includes a machine (e.g., a computer) readablestorage medium (e.g., read only memory (“ROM”), random access memory(“RAM”), magnetic disk storage media, optical storage media, flashmemory devices).

The processes or methods depicted in the preceding figures may beperformed by processing logic that comprises hardware (e.g. circuitry,dedicated logic, etc.), software (e.g., embodied on a non-transitorycomputer readable medium), or a combination of both. Although theprocesses or methods are described above in terms of some sequentialoperations, it should be appreciated that some of the operationsdescribed may be performed in a different order. Moreover, someoperations may be performed in parallel rather than sequentially.

Embodiments of the present disclosure are not described with referenceto any particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof embodiments of the disclosure as described herein.

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to specific exemplary embodiments thereof. Itwill be evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope of the disclosure as setforth in the following claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

What is claimed is:
 1. A computer-implemented method to generate adriving trajectory for an autonomous driving vehicle (ADV), the methodcomprising: calculating a first trajectory based on map and routeinformation; and performing a path optimization based on the firsttrajectory, traffic rules, and obstacle information describing obstaclesperceived by the ADV, the path optimization including: performing aspline curve based path optimization on the first trajectory, includingminimizing a path cost function for a path spline curve to determine oneor more polynomials that form the path spline curve to form an optimizedpath curve; determining whether the spline curve based path optimizationcannot complete; when the spline curve based path optimization on thefirst trajectory cannot complete, then performing a finite element basedpath optimization on the first trajectory, including segmenting thefirst trajectory into a plurality of path segments, and minimizingdiscontinuities between adjacent path segments in the plurality of pathsegments, resulting in the optimized path curve; performing a speedoptimization based on a result of the path optimization including:performing a spline curve based speed optimization on the firsttrajectory in view of the optimized path curve, including minimizing aspeed cost function for a speed spline curve to determine one or morepolynomials that form the speed spline curve to form an optimized speedcurve; determining whether the spline curve based speed optimizationcannot complete, when the spline curve based speed optimization cannotcomplete, then performing a finite element based speed optimization onthe first trajectory in view of the optimized path curve, includingreducing changes in acceleration and speed between adjacent controlpoints on the speed spline curve to form the optimized speed curve;generating a second trajectory based on the optimized path curve and theoptimized speed curve, wherein the second trajectory is a drivingtrajectory that includes coordinates used to control the ADV to passthrough; and operating the ADV using the second trajectory.
 2. Thecomputer-implemented method of claim 1, wherein the speed optimizationis performed based on an initial speed of the ADV and a speed limit. 3.The computer-implemented method of claim 2, wherein the optimized pathcurve includes a radial path and the speed optimization is performedbased on a radius of the radial path.
 4. The computer-implemented methodof claim 1, wherein determining whether the spline curve based pathoptimization can complete comprises determining if a number of iterativecalculations of the spline curve based path optimization exceeds apredetermined number.
 5. The computer-implemented method of claim 1,wherein determining whether the spline curve based path optimization cancomplete comprises determining if a time spent for the spline curvebased path optimization exceeds a predetermined period of time.
 6. Thecomputer-implemented method of claim 1, wherein minimizing thediscontinuities between the adjacent path segments in the plurality ofpath segments includes minimizing discontinuity of curvature between theadjacent path segments.
 7. A non-transitory machine-readable mediumhaving instructions stored therein, which when executed by one or moreprocessors, cause the one or more processors to perform operations togenerate a driving trajectory for an autonomous driving vehicle (ADV),the operations comprising: calculating a first trajectory based on mapand route information; and performing a path optimization based on thefirst trajectory, traffic rules, and obstacle information describingobstacles perceived by the ADV, including: performing a spline curvebased path optimization on the first trajectory including minimizing apath cost function for a path spline curve to determine one or morepolynomials that form the path spline curve to form an optimized pathcurve; determining whether the spline curve based path optimizationcannot complete; when the spline curve based path optimization on thefirst trajectory cannot complete, then performing a finite element basedpath optimization on the first trajectory including segmenting the firsttrajectory into a plurality of path segments, and minimizingdiscontinuities between adjacent path segments in the plurality of pathsegments resulting in the optimized path curve; performing a speedoptimization based on a result of the path optimization including:performing a spline curve based speed optimization on the firsttrajectory in view of the optimized path curve, including minimizing aspeed cost function for a speed spline curve to determine one or morepolynomials that form the speed spline curve to form an optimized speedcurve; determining whether the spline curve based speed optimizationcannot complete, when the spline curve based speed optimization cannotcomplete, then performing a finite element based speed optimization onthe first trajectory in view of the optimized path curve, includingreducing changes in acceleration and speed between adjacent controlpoints on the speed spline curve to form the optimized speed curve;generating a second trajectory based on the optimized path curve and theoptimized speed curve, wherein the second trajectory is a drivingtrajectory that includes coordinates used to control the ADV to passthrough; and operating the ADV using the second trajectory.
 8. Thenon-transitory machine-readable medium of claim 7, wherein the speedoptimization is performed based on an initial speed of the ADV and aspeed limit.
 9. The non-transitory machine-readable medium of claim 8,wherein the optimized path curve includes a radial path and the speedoptimization is performed based on a radius of the radial path.
 10. Thenon-transitory machine-readable medium of claim 7, wherein determiningwhether the spline curve based path optimization can complete comprisesdetermining if a number of iterative calculations of the spline curvebased path optimization exceeds a predetermined number.
 11. Thenon-transitory machine-readable medium of claim 7, wherein determiningwhether the spline curve based path optimization can complete comprisesdetermining if a time spent for the spline curve based path optimizationexceeds a predetermined period of time.
 12. The non-transitorymachine-readable medium of claim 7, wherein minimizing thediscontinuities between the adjacent path segments in the plurality ofpath segments includes minimizing discontinuity of curvature between theadjacent path segments.
 13. A data processing system, comprising: one ormore processors; and a memory coupled to the one or more processors tostore instructions, which when executed by the one or more processors,cause the one or more processors to perform operations to generate adriving trajectory for an autonomous driving vehicle (ADV), theoperations including calculating a first trajectory based on map androute information; and performing a path optimization based on the firsttrajectory, traffic rules, and obstacle information describing obstaclesperceived by the ADV, including: performing a spline curve based pathoptimization on the first trajectory including minimizing a path costfunction for a path spline curve to determine one or more polynomialsthat form the path spline curve to form an optimized path curve;determining whether the spline curve based path optimization cannotcomplete; when the spline curve based path optimization on the firsttrajectory cannot complete, then performing a finite element based pathoptimization on the first trajectory, including segmenting the firsttrajectory into a plurality of path segments, and minimizingdiscontinuities between adjacent path segments in the plurality of pathsegments, resulting in the optimized path curve; performing a speedoptimization based on a result of the path optimization including:performing a spline curve based speed optimization on the firsttrajectory in view of the optimized path curve, including minimizing aspeed cost function for a speed spline curve to determine one or morepolynomials that form the speed spline curve to form an optimized speedcurve; determining whether the spline curve based speed optimizationcannot complete, when the spline curve based speed optimization cannotcomplete then performing a finite element based speed optimization onthe first trajectory in view of the optimized path curve, includingreducing changes in acceleration and speed between adjacent controlpoints on the speed spline curve to form the optimized speed curve;generating a second trajectory based on the optimized path curve and theoptimized speed curve, wherein the second trajectory is used to controlthe ADV; and operating the ADV using the second trajectory.
 14. Thesystem of claim 13, wherein the speed optimization is performed based onan initial speed of the ADV and a speed limit.
 15. The system of claim14, wherein the optimized path curve includes a radial path and thespeed optimization is performed based on a radius of the radial path.16. The system of claim 13, wherein determining whether the spline curvebased path optimization can complete comprises determining if a numberof iterative calculations of the spline curve based path optimizationexceeds a predetermined number.
 17. The system of claim 13, whereindetermining whether the spline curve based path optimization cancomplete comprises determining if a time spent for the spline curvebased path optimization exceeds a predetermined period of time.
 18. Thesystem of claim 13, wherein minimizing the discontinuities between theadjacent path segments in the plurality of path segments includesminimizing discontinuity of curvature between the adjacent pathsegments.